Search: “llm”

When Plans Change Answers: Formalizing Cost-Accuracy Optimization for Semantic Queries

arXiv:2610.08089v1 Announce Type: cross Abstract: In semantic query engines, predicates are evaluated by machine-learned models, and the choice of a query plan affects not only the cost of a query but

safetyarxiv-cs-ai

7 Oct 2026

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Agents

When to Remember, When to Abstain: Category-Conditioned Retention for Reliable Agent Memory

DGX agent

arXiv:2610.07100v1 Announce Type: new Abstract: Persistent agent memory is only as reliable as its retention decision: an assertion weakly supported by its source can be stored and later reused as est

agentsarxiv-cs-ai

7 Oct 2026

Agentic Resource Allocation for Batch Multi-Objective Bayesian Optimization in Autonomous Materials Discovery

arXiv:2610.04134v1 Announce Type: cross Abstract: The discovery and development of advanced materials is a challenging process constrained by the high time and monetary costs of synthesis, processing,

Agentic-ZTA: A Multi-Agent Architecture for Autonomous Zero Trust Enforcement

arXiv:2610.05782v1 Announce Type: cross Abstract: Agentic AI is emerging as a promising paradigm for automating complex cybersecurity decisions, yet its use in enforcing zero trust introduces signific

Aligning the Query Space: Greedy Information Projection for Language Model Data Selection

arXiv:2603.13790v2 Announce Type: replace Abstract: Data selection for language models is often framed as balancing example quality and diversity. We argue that both are consequences of a more fundame

Answer Set Networks: Casting Answer Set Programming into Deep Learning

arXiv:2412.14814v2 Announce Type: replace-cross Abstract: Although Answer Set Programming (ASP) allows constraining neural-symbolic (NeSy) systems, its employment is hindered by the prohibitive costs

hardwarearxiv-cs-lg

Anticipating the Consequences of Curriculum Decisions with Large Language Models

arXiv:2610.04604v1 Announce Type: new Abstract: Automatic curriculum learning can improve the effectiveness of reinforcement learning by selecting the training experiences presented to the agent over

Better Call Reward: Reward Hacking as Strategic Abstention in Legal Reasoning Models

arXiv:2610.06439v1 Announce Type: new Abstract: What happens when a legal AI model learns to look like a lawyer instead of reasoning like one? We fine tune Qwen3-8B with Group Relative Policy Optimisa

Causally Fair Generation with Large Language Models

arXiv:2610.04444v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate, complete, and transform information in settings where their outputs can shape conseque

ColdDDI: Evaluating Knowledge Utilization in Cold-Start Drug-Drug Interaction Prediction

arXiv:2610.05590v1 Announce Type: new Abstract: Cold-start drug-drug interaction (DDI) prediction tests whether models can identify clinically significant interactions for drugs without training-time

COMIC: Agentic Sketch Comedy Generation

arXiv:2603.11048v2 Announce Type: replace Abstract: We propose a fully automated AI system that produces short comedic videos similar to sketch shows such as Saturday Night Live. Starting from charact

agentsarxiv-cs-cv

CURIO: Curiosity-Driven Test-Time Learning for Open-Ended Discovery

arXiv:2610.04851v1 Announce Type: new Abstract: Open-ended discovery requires learning from repeated attempts while continuing to explore directions whose value is not yet apparent. Search with a froz

DelegationBench: Measuring When AI Agents Should Ask Before Acting

arXiv:2610.05532v1 Announce Type: cross Abstract: AI agents that send emails, edit files, and make purchases must decide when to act on their own and when to check with the user first. This decision i

DiffGate: Difficulty-Gated Teacher Guidance for On-Policy Distillation

arXiv:2610.04596v1 Announce Type: new Abstract: On-policy distillation (OPD) has emerged as a widely used paradigm for post-training large language models, reducing the train--test mismatch of convent

local-aiarxiv-cs-lg

Distributed Subliminal Learning: Replacing Model Updates with Random-Carrier Outputs

arXiv:2610.05378v1 Announce Type: new Abstract: Collaborative learning typically exchanges model parameters: federated clients communicate updates, while independently adapted foundation models are co

local-aiarxiv-cs-lg

Dynamic Harness Search: Building Multi-Agent Systems Per-Query via Prediction

arXiv:2610.04137v1 Announce Type: new Abstract: Agent harnesses specify the roles, instructions, tools, and communication structure used to solve a task, and the right harness depends on the query. Be

E^2-OPSD: Taming Entropy Overshoot in On-Policy Self-Distillation

arXiv:2610.05048v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) provides dense token-level supervision without a second model: one network acts as teacher with the reference solutio

Energy Variation in Training Modern Computer Vision Architectures

arXiv:2610.03772v1 Announce Type: new Abstract: The rapid growth of deep learning has substantially increased the energy consumption associated with model training, making energy efficiency an increas

hardwarearxiv-cs-cv

Frozen Factor or Spectral Band? Disentangling Two Choices in Low-Rank LoRA

arXiv:2610.06621v1 Announce Type: new Abstract: Spectral variants of low-rank adaptation (LoRA) choose both a subspace and which factor to freeze. We separate these choices by freezing the input facto

hardwarearxiv-cs-lg

Human Behavior-Informed Crash Scenario Generation with Real-World Crash Priors for Autonomous Vehicle Safety Evaluation

arXiv:2610.04366v1 Announce Type: new Abstract: Reliable safety evaluation of autonomous vehicles (AVs) is essential to improving road safety, yet it depends critically on realistic simulation of rare

safetyarxiv-cs-ro

Human-in-the-Loop Neuro-Symbolic Drift Anticipation for Reliable Visual SLAM

arXiv:2610.05757v1 Announce Type: new Abstract: This paper introduces Hybrid DeepSEE (HDS), a Human-in-the-Loop (HITL) neuro-symbolic framework for proactive drift anticipation in Visual SLAM (V-SLAM)

researcharxiv-cs-ro

Latent Safety Filters: When a Lossy Encoder Admits a Transferable Certificate

arXiv:2610.04297v1 Announce Type: cross Abstract: Latent safety filters certify safety on a learned low-dimensional representation of the state, enabling constraints that resist analytic description.

Learning without Overwriting: A Theory of Self-Distillation and Supervised Fine-Tuning in Continual Reasoning

arXiv:2610.05200v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) of large language models (LLMs) has demonstrated the ability to improve reasoning capabilities while preserving previ

Lens3D: Target-Conditioned Visual Foveation for Fine-Grained 3D Understanding

arXiv:2610.06611v1 Announce Type: new Abstract: Existing 3D large language models often overlook fine-grained attributes and less visually salient objects and parts, even when relevant evidence is pre

local-aiarxiv-cs-cv

MemLeak: Cross-User Semantic Leakage in Multi-Tenant AI Agent Memory

arXiv:2610.04195v1 Announce Type: cross Abstract: Personal AI agents in enterprise multi-tenant deployments share a common vector store for long-term memory. Shared embedding spaces create a surface f

MetaKernelBench: Measuring GPU Kernel Knowledge Transfer Beyond Code

arXiv:2610.05014v1 Announce Type: new Abstract: Recent GPU kernel optimization agents retain what they learn in knowledge bases or as distilled skills. Kernel benchmarks score each attempt's implement

hardwarearxiv-cs-lg

Mind the Gaps: From Failure Attribution to Closed-Form Repair of Code Language Models

arXiv:2610.05277v1 Announce Type: cross Abstract: Code language models must be maintained like the software around them: when a library evolves, a model keeps writing the interface that it saw during

Tutorials

One Token Can Be Enough: Bridging Prompting and Activation Steering with Prefix Steering

arXiv:2610.04967v1 Announce Type: new Abstract: Prompting guides language model behavior through the initial context, whereas activation steering often intervenes throughout generation. A natural ques

tutorialsarxiv-cs-lg

Outcome-Guided On-Policy Self-Distillation

arXiv:2610.05070v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) provides denser token-level supervision and better computational efficiency than Reinforcement Learning with Verifiab

OVAL: Output-Aware Local Page Bases for KV Cache Retrieval

arXiv:2610.06686v1 Announce Type: new Abstract: Long context inference with large language models becomes increasingly expensive as attention must operate over an ever growing KV cache. Page sparse at

PyINE: A Framework for Scalable Elicitation and Oversight via Code Execution

arXiv:2610.04737v1 Announce Type: cross Abstract: Reasoning models can remain capable of solving a task while still defaulting to cheaper but misleading shortcuts. This creates a central oversight pro

Rethinking Self-Distillation for Multi-Teacher Capability Merging

arXiv:2610.04272v1 Announce Type: new Abstract: Combining capabilities of multiple expert models trained starting from the same base checkpoint has become increasingly common in frontier language-mode

Tutorials

Robotic Long-Horizon Manipulation with Progressive In-Context Code Generation and Episodic Feedback

arXiv:2503.21969v4 Announce Type: replace Abstract: Robotic long-horizon manipulation requires robots to compose perception, reasoning, and action over extended task sequences, yet existing language-c

tutorialsarxiv-cs-ro

RoSA: Rotational Sparse Adaptation for Memory-Efficient Fine-Tuning

arXiv:2610.06243v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) reduces the cost of adapting foundation models by focusing training on a small parameter subset. Complementary to

SIFT: Robust Meta-Faithfulness Verification of Chain-of-Thought Reasoning Under Distribution Shift

arXiv:2610.04594v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) faithfulness detectors are widely used to audit reasoning models, yet a detector is itself a predictor whose verdicts are treat

Software World Models: From Consequence Prediction to Decision Value

arXiv:2610.04940v1 Announce Type: new Abstract: A coding agent may safely modify one repository while silently breaking downstream services, libraries, or datastores that depend on it. Exhaustively ru

agentsarxiv-cs-lg

SpatialChain: A Benchmark for Auditing Spatial Reasoning Faithfulness in VLMs

model-releasesarxiv-cs-cv

arXiv:2610.06413v1 Announce Type: new Abstract: Thinking-enabled vision-language models (VLMs) report ever-higher accuracy on spatial benchmarks, yet final-answer scores cannot reveal whether a correc

Synthesizing Physics Formulae with Transformers

arXiv:2610.03947v1 Announce Type: new Abstract: Finding a compact formula that fits a set of input-output pairs and predicts outputs on unseen inputs is a fundamental problem in science. Symbolic regr

The Numerical Linear Algebra of Large Language Models

arXiv:2610.04631v1 Announce Type: new Abstract: Numerical Linear Algebra (NLA) has consistently played a vital role in advancing science by providing tools to solve fundamental problems encountered in

The Optimization Landscape of Learning Compacted Context Models

arXiv:2610.05885v1 Announce Type: new Abstract: Many works approach continual learning through the lens of infinite context windows. As an agent puts more observation into context (concretely the KV c

agentsarxiv-cs-lg

ThunderSyncRL: Lossless Acceleration of Agentic Reinforcement Learning

arXiv:2610.05935v1 Announce Type: new Abstract: Language models are moving beyond generating answers to pursuing long-horizon goals in interactive environments. Post-training these agents requires lon

Transfer-Stratified On-Policy Distillation for RL-Improved Reasoning Teachers

arXiv:2610.05974v1 Announce Type: new Abstract: Reinforcement learning can substantially improve a reasoning teacher, but it is unclear which of those improvements survive when the teacher supervises

What Does a Harness Buy? Tokens, Mostly

arXiv:2610.04433v1 Announce Type: cross Abstract: A coding agent is a language model wrapped in a harness: the system prompt, the tool set, and the context management that turn a chat model into somet

What May an Agent Change About Itself? A Containment Floor for Self-Configuring Agent Runtimes

arXiv:2610.06274v1 Announce Type: new Abstract: Many agent runtimes give the agent a tool for editing its own configuration. Some of that configuration grants abilities, such as enabling a tool. Other

agentsarxiv-cs-lg

Which Preferences to Train On? End-to-End Multi-Objective Alignment with an Adversarial Preference Distribution

arXiv:2610.04845v1 Announce Type: new Abstract: Aligning large language models (LLMs) with human values is important for safe, efficient, and beneficial AI deployment. However, human values are multif

Adaptive Mutual Distillation for Balanced Multi-Task Post-Training of Large Language Models

model-releasesarxiv-cs-cl

arXiv:2610.02856v1 Announce Type: new Abstract: Multi-task post-training of large language models (LLMs) aims to improve performance across tasks with unequal amounts of training data. Existing method

5 Oct 2026

AgentTrap: Stateful Feedback Deception against Autonomous Penetration Testing Agents

arXiv:2610.02869v1 Announce Type: cross Abstract: Autonomous penetration testing agents conduct multi-step attacks by continuously adapting their plans and actions to target responses. As a common def

agentsarxiv-cs-ai

5 Oct 2026

AI-driven Thermal-aware Data Center Capacity Planning